An Empirical Study of Derivative-Free-Optimization Algorithms for Targeted Black-Box Attacks in Deep Neural Networks
This study provides insights into the effectiveness of different black-box attack algorithms for researchers and practitioners developing and evaluating adversarial defenses, highlighting the importance of diverse testing.
This paper empirically studies derivative-free optimization (DFO) algorithms for targeted black-box adversarial attacks on Deep Neural Networks under L-infinity constraints and limited queries. The study compares four existing DFO algorithms and introduces a new BOBYQA-based algorithm, finding that algorithms limiting search to L-infinity constraint vertices perform well without structural defenses, while the new BOBYQA algorithm excels at small perturbation energies.
We perform a comprehensive study on the performance of derivative free optimization (DFO) algorithms for the generation of targeted black-box adversarial attacks on Deep Neural Network (DNN) classifiers assuming the perturbation energy is bounded by an $\ell_\infty$ constraint and the number of queries to the network is limited. This paper considers four pre-existing state-of-the-art DFO-based algorithms along with the introduction of a new algorithm built on BOBYQA, a model-based DFO method. We compare these algorithms in a variety of settings according to the fraction of images that they successfully misclassify given a maximum number of queries to the DNN. The experiments disclose how the likelihood of finding an adversarial example depends on both the algorithm used and the setting of the attack; algorithms limiting the search of adversarial example to the vertices of the $\ell^\infty$ constraint work particularly well without structural defenses, while the presented BOBYQA based algorithm works better for especially small perturbation energies. This variance in performance highlights the importance of new algorithms being compared to the state-of-the-art in a variety of settings, and the effectiveness of adversarial defenses being tested using as wide a range of algorithms as possible.